First vegetation optical depth product retrieved from FengYun-3D X-band observations

Vegetation Optical Depth (VOD) is an essential variable for monitoring global vegetation biomass and water content. However, accurately retrieving VOD from satellite microwave observations poses challenges due to discrepancies in the parameterization of the microwave radiative transfer model. The two most uncertain parameters are the effective scattering albedo and soil surface roughness, which vary across ecosystems but are often treated as global constants in most existing approaches. The daily X-band VOD (X-VOD) at a spatial resolution of 0.25° during 2022–2024 is retrieved from FengYun-3D (FY-3D) microwave brightness temperature observations, representing the first FY-3D-based X-VOD product. The vegetation scattering albedo and soil roughness parameters in the radiative transfer model are calibrated using this methodology. In this calibration process, ground-based soil moisture (SM) observations and soil moisture estimates derived from the ECMWF Reanalysis 5 (ERA5) dataset were employed as constraints to improve the accuracy and reliability of the retrieval algorithm. The FY-3D X-VOD exhibits strong consistency with commonly used vegetation-related variables, including aboveground biomass, tree cover, canopy height, as well as optical vegetation indices such as Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Leaf Area Index, with spatial correlation coefficients ranging from 0.74 to 0.86. In contrast, the correlations of these vegetation variables with AMSR-E/2-based X-VOD products are lower, with values of 0.66–0.80 for Land Parameter Data Record and 0.57–0.72 for Land Parameter Retrieval Model. In terms of temporal dynamics, FY-3D X-VOD also shows comparable or improved correlation with optical vegetation indices. For instance, with respect to EVI derived from the MODIS sensor (MOD13A2 V6.1 product), FY-3D X-VOD exhibits the highest temporal correlation in 44.7% of globally vegetated areas, followed by LPDR (37.2%) and LPRM (11.7%). Additionally, FY-3D X-VOD shows potential for monitoring forest loss across diverse forest types. This first FY-3D X-VOD product expands the sources of global microwave vegetation monitoring and demonstrates great potential for studying ecosystem dynamics, climate impacts, and agricultural applications.

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Journal
Remote Sensing of Environment
Published
2026-09-14
DOI
https://doi.org/10.1016/j.rse.2026.115647
Primary Topic
Soil Moisture and Remote Sensing
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article
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First vegetation optical depth product retrieved from FengYun-3D X-band observations

Dongbo Chen, Yao Xiao, Xiangzhuo Liu, Frédéric Frappart et al.
Remote Sensing of Environment
Soil Moisture and Remote Sensing
article

First vegetation optical depth product retrieved from FengYun-3D X-band observations

Dongbo Chen, Yao Xiao, Xiangzhuo Liu, Frédéric Frappart, Ronghan Xu, Jian Peng, Gabrielle De Lannoy, Xiuqing Hu, Mengjia Wang, Jiangyuan Zeng, Xin Li, Lei Fan, Philippe Ciais, Jean-Pierre Wigneron, Xiaojun Li
article en

Abstract

Vegetation Optical Depth (VOD) is an essential variable for monitoring global vegetation biomass and water content. However, accurately retrieving VOD from satellite microwave observations poses challenges due to discrepancies in the parameterization of the microwave radiative transfer model. The two most uncertain parameters are the effective scattering albedo and soil surface roughness, which vary across ecosystems but are often treated as global constants in most existing approaches. The daily X-band VOD (X-VOD) at a spatial resolution of 0.25° during 2022–2024 is retrieved from FengYun-3D (FY-3D) microwave brightness temperature observations, representing the first FY-3D-based X-VOD product. The vegetation scattering albedo and soil roughness parameters in the radiative transfer model are calibrated using this methodology. In this calibration process, ground-based soil moisture (SM) observations and soil moisture estimates derived from the ECMWF Reanalysis 5 (ERA5) dataset were employed as constraints to improve the accuracy and reliability of the retrieval algorithm. The FY-3D X-VOD exhibits strong consistency with commonly used vegetation-related variables, including aboveground biomass, tree cover, canopy height, as well as optical vegetation indices such as Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Leaf Area Index, with spatial correlation coefficients ranging from 0.74 to 0.86. In contrast, the correlations of these vegetation variables with AMSR-E/2-based X-VOD products are lower, with values of 0.66–0.80 for Land Parameter Data Record and 0.57–0.72 for Land Parameter Retrieval Model. In terms of temporal dynamics, FY-3D X-VOD also shows comparable or improved correlation with optical vegetation indices. For instance, with respect to EVI derived from the MODIS sensor (MOD13A2 V6.1 product), FY-3D X-VOD exhibits the highest temporal correlation in 44.7% of globally vegetated areas, followed by LPDR (37.2%) and LPRM (11.7%). Additionally, FY-3D X-VOD shows potential for monitoring forest loss across diverse forest types. This first FY-3D X-VOD product expands the sources of global microwave vegetation monitoring and demonstrates great potential for studying ecosystem dynamics, climate impacts, and agricultural applications.

Remote Sensing of EnvironmentVol. 347
Centre National de la Recherche Scientifique (FR), Helmholtz Centre for Environmental Research (DE), China Meteorological Administration (CN), Southwest University (CN), Université de Bordeaux (FR), Université de Versailles Saint-Quentin-en-Yvelines (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris-Saclay (FR), Zhengzhou University (CN), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR), Biologie du Fruit et Pathologie (FR), Interaction Sol Plante Atmosphère (FR), Laboratoire des Sciences du Climat et de l'Environnement (FR), Institute of Remote Sensing and Digital Earth (CN), CEA Paris-Saclay (FR), Unité mixte de recherche Œnologie (FR), Aerospace Information Research Institute (CN), Chongqing Airport, Southwest Jiaotong University (CN), Leipzig University (DE)
Life in Land
Openalex Percentile: Top 18%
Soil Moisture and Remote Sensing
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